Research outputs generated with public funding should be accessible for reuse. In the scientific process, many different kinds of output are generated, depending on the scientific discipline, the sources of data and the type of analyses that researchers perform. For sharing and reusing data in the open science environment, it is important to provide clarity about the quality of the data offered and to have effective agreements in place for better reuse of data. If data is to be archived and made suitable for reuse, it must be clear to third parties how the data is structured and what information it contains.

Create optimal conditions for sharing research output by introducing a quality hallmark for the FAIR principles, data, and data management requirements: research output should be Findable, Accessible, Interoperable and Reusable.

Concrete actions

National authorities and the European Commission: state that research output produced with public funding should, in principle, be accessible for reuse. Promote the FAIR principles. Provide for a bottom-up and discipline-based approach and elaboration.

National authorities and Research Performing Organisations: put in place an institutional data policy which clarifies institutional roles and responsibilities for research data management and data stewardship.

Research funders: implement Data Management Plans (DMPs) as an integral part of the research process, make them a precondition for funding, standardise them and make the costs incurred eligible for funding.

Research funders: introduce positive incentives for FAIR data sharing by valuing data stewardship and efforts to make data available and by acknowledging and rewarding those who compile the data. Require data to be cited according to international standards. Encourage the sharing of expertise that enables disciplines/regions to learn from each other.

Research funders: set the default in data sharing to open access, but allow a choice of access regimes: from open and free downloads to application and registration-based access. Conditions can be dependent on the nature of the data, common practice within a specific academic discipline, legal (privacy) frameworks, and legitimate interests of the parties involved.

National authorities and research funders: educate data stewardship experts, recognise their profession and provide them with career opportunities. They will act as a bridge between IT and science.

Expected positive effects

Increased quality of research;

Better adherence to the principles of good scientific research and conduct to foster research integrity;

Increased impact of publicly funded research;

Secure sharing and reuse of research outputs, which will foster science and innovation.|

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9 Comments

Anonymous

Remove reference to "legitimate interests of the parties involved" as this is too open to interpretation and abuse. Open should be the default for data.

There is also a need for investment for local support for data management. Data management plans should be made open.

Anonymous

Privacy- and security-sensitive research data mandate a registration-based form of access, so as to a. keep track of who is using the data; and b. retract access when this needed.

Under no circumstance should such data be freely downloadable.

Martin Stokhof, on behalf of the Open Access Working Group of the European Research Council

Proposed changes:

The problem

Research outputs generated with public funding should be accessible for reuse, unless IPR, privacy and security considerations dictate differently. In the scientific process, many different kinds of output are generated, depending on the scientific discipline, the sources of data and the type of analyses that researchers perform. For sharing and reusing data in the open science environment, it is important to provide clarity about the quality of the data offered, the IPR-, privacy- and security-sensitivities that the data may carry, and to have effective agreements in place for better reuse of data. If data is to be archived and made suitable for reuse, it must be clear to third parties how the data is structured, what information it contains, and what constraints may follow from any IPR-, privacy- and data-sensitivities.

The solution

Develop Principles & Guidelines for Data Management Plans and data stewardship, including measures for future-proofing data for privacy- and security-risks.

Create optimal conditions for sharing research output by introducing a quality hallmark for the FAIR principles, data, and data management requirements: research output should be Findable, Accessible, Interoperable and Reusable, unless IPR, privacy and security reasons dictate differently.

Concrete actions

National authorities and the European Commission: state that research output produced with public funding should be accessible for reuse, unless IPR, privacy and security reasons dictate differently. Promote the FAIR principles. Provide for a bottom-up and discipline-based approach and elaboration.

National authorities and Research Performing Organisations: put in place an institutional data policy which clarifies institutional roles and responsibilities for research data management and data stewardship including measures for future-proofing data for privacy- and security-risks.

Research funders: implement Data Management Plans (DMPs) as an integral part of the research process, make them a precondition for funding, standardise them and make the costs incurred eligible for funding.

Research funders: introduce positive incentives for FAIR data sharing by valuing data stewardship and efforts to make data available and by acknowledging and rewarding those who compile the data. Require data to be cited according to international standards. Encourage the sharing of expertise that enables disciplines/regions to learn from each other.

Research funders: set the default in data sharing to open access, but allow a choice of access regimes: from open and free downloads to application and registration-based access. Conditions can be dependent on the nature of the data, common practice within a specific academic discipline, legal (privacy) frameworks, and legitimate interests of the parties involved.

National authorities and research funders: educate data stewardship experts, recognise their profession and provide them with career opportunities. They will act as a bridge between IT and science.

Expected positive effects

Increased quality of research;

Better adherence to the principles of good scientific research and conduct to foster research integrity;

Increased impact of publicly funded research;

Secure sharing and reuse of research outputs, which will foster science and innovation.|

Anonymous

I'd like to suggest an additional concrete action:

Concrete actions Research funders: implement Data Management Plans (DMPs) as an integral part of the research process, make them a precondition for funding, standardise them and fund the associated costs for data sharing as an integral part of funding the research.

Anonymous

Under 'Concrete actions':

I propose changing the third bullet point as follows:

Research funders: implement Data Management Plans (DMPs) as an integral part of the research process, make them a precondition for funding, standardise them in a way that allows researchers from different disciplines to take into account specificities from their respective fields, and make the costs incurred eligible for funding.

Add as concrete actions:

Research funders: support the training of (in particular early career) researchers on issues related to data management and sharing. Make related costs eligible within research projects.

Anonymous

Michael Matlosz, President Science Europe:

I would stress that is important for all parties to take into account the full research cycle and data life cycle, and not just the phases of the actual research project when formulating policies with respect to the funding of RDM facilities - the challenge is mainly in the sustainability of the results after the funding of a project has ended.

Anonymous

Anonymous

We agree that FAIR should be promoted and definitions of FAIR at a disciplinary level pursued. However there are cross-disciplinary and cross-domain standards that should be supported. Implementation and development of FAIR should also be pursued via information and infrastructure professionals in concert and in a complementary way to the development of the disciplinary standards.

A group of major UK funders and other stakeholders have been working on a draft Data Concordat (the UK Open Research Data Forum, http://www.rcuk.ac.uk/research/opendata/ ), which could serve as a basis for a wider agreement. Recognition for data sharing as an academic practice ties back to point one, and we would be keen to see support for existing work such as the Data Citation Implementation Group at FORCE11. We are pleased to see that the costs of data sharing are considered – many institutions are reporting concerns around the mismatch between short-term project funding and long-term curation/archival costs and it is essential that the costs and sustainability issues of research data and wider research objects are addressed, along with the infrastructure. OECD are looking at this issue, and there are various initiatives tackling this as far as possible at a local level; we would welcome active engagement on the ways to address this practically across member states with work between infrastructure providers, funders and research performing organisations. This might be similar to the sustainability initiatives underway for open access infrastructure referred to in the infrastructure section.

We agree that data management plans are important, and we would advocate machine readable and actionable plans to help to meet a range of use cases. Some work has been done on data definitions (https://jisccasraipilot.jiscinvolve.org/wp/working-groups/data-management-plans/) but this needs to be implemented, further developed and working together internationally on- this is the ideal. Actions around the monitoring of open science progress can be enhanced by machine actionable data management plans and policies.